Statistics is about building tools for data analysis and statisticians are tool makers, tool users and tools themselves. If modern Universities had optics departments for making telescopes and microscopes, Statistics would fit comfortably in the same division. Statistics and computer science are natural allies as computer science exists to make tools for people to apply in many different situations. Statistics makes tools for people to apply to data analysis, statistical inference, decision making and prediction.

Statistics exists to be applied to data. If not for applications, there would be little point to Statistics as a discipline. Statistics is a separate discipline and not a sub-discipline of other fields because the tools that we develop are useful across many disciplines.

I have both biostatistical projects and collaborative research projects.

My biostatistical research is developing Bayesian methods for modeling data. I like to develop statistical models, Bayesian computing algorithms, and model selection techniques. I particularly like figuring out the issues involved with analyzing longitudinal data, meta-analysis, hierarchical models, semi-parametric Bayesian modeling and multivariate longitudinal data analysis. In the past, I have developed statistical diagnostics for residual, influence and sensitivity analysis. I work on statistical graphics, prior specification and hierarchical models. Most of my papers combine several of these topics.

Collaborative or applied research is where statisticians apply their expertise to data analysis with the researchers who collected the data.

My applied research involves data and problems from HIV research and emergency medicine and any area where I can help out an interesting researcher with an interesting applied problem.

I especially enjoy teaching my classes in Statistical Graphics, Longitudinal Data, Theoretical Bayesian Methods, and Applied Bayesian Analysis. I have a text book on Modeling Longitudinal Data (2005, Springer) that we use in my longitudinal data class. All of my classes have computing and writing components. There isn't much point to statistical practice unless you can calculate what you need to on the computer and communicate what you have learned. I particularly like working in an academic environment because I get to work with a variety of subject matter researchers and Biostatistics graduate students on research projects.

**-- Rob Weiss**